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In this work, we propose a framework that creates a lively virtual dynamic scene with contextual motions of multiple humans. Generating multi-human contextual motion requires holistic reasoning over dynamic relationships among human-human…

Computer Vision and Pattern Recognition · Computer Science 2025-07-28 Donggeun Lim , Jinseok Bae , Inwoo Hwang , Seungmin Lee , Hwanhee Lee , Young Min Kim

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object…

Computation and Language · Computer Science 2015-06-08 Angel Chang , Will Monroe , Manolis Savva , Christopher Potts , Christopher D. Manning

Generating realistic 3D indoor scenes from user inputs remains a challenging problem in computer vision and graphics, requiring careful balance of geometric consistency, spatial relationships, and visual realism. While neural generation…

Computer Vision and Pattern Recognition · Computer Science 2025-06-30 Mengqi Zhou , Xipeng Wang , Yuxi Wang , Zhaoxiang Zhang

We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision-language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes.…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Guying Lin , Kemeng Huang , Michael Liu , Ruihan Gao , Hanke Chen , Lyuhao Chen , Beijia Lu , Taku Komura , Yuan Liu , Jun-Yan Zhu , Minchen Li

The generation of 3D scenes from user-specified conditions offers a promising avenue for alleviating the production burden in 3D applications. Previous studies required significant effort to realize the desired scene, owing to limited…

Computer Vision and Pattern Recognition · Computer Science 2024-11-28 Takayuki Hara , Tatsuya Harada

We present 3DScenePrompt, a framework that generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. Unlike methods conditioned on a single image or a short clip, we…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 JoungBin Lee , Jaewoo Jung , Jisang Han , Takuya Narihira , Kazumi Fukuda , Junyoung Seo , Sunghwan Hong , Yuki Mitsufuji , Seungryong Kim

We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene…

Computer Vision and Pattern Recognition · Computer Science 2020-04-21 Yan Zhang , Mohamed Hassan , Heiko Neumann , Michael J. Black , Siyu Tang

Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a challenging task, also due to the inherent difficulty in…

Computer Vision and Pattern Recognition · Computer Science 2021-09-17 Enrico Meloni , Alessandro Betti , Lapo Faggi , Simone Marullo , Matteo Tiezzi , Stefano Melacci

Well-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently…

Human-Computer Interaction · Computer Science 2026-03-04 Semin Jin , Donghyuk Kim , Jeongmin Ryu , Kyung Hoon Hyun

Synthesizing interactive 3D scenes from text is essential for gaming, virtual reality, and embodied AI. However, existing methods face several challenges. Learning-based approaches depend on small-scale indoor datasets, limiting the scene…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Lu Ling , Chen-Hsuan Lin , Tsung-Yi Lin , Yifan Ding , Yu Zeng , Yichen Sheng , Yunhao Ge , Ming-Yu Liu , Aniket Bera , Zhaoshuo Li

Synthetic 3D scenes are essential for developing Physical AI and generative models. Existing procedural generation methods often have low output throughput, creating a significant bottleneck in scaling up dataset creation. In this work, we…

Robotics · Computer Science 2025-12-19 Jinghuan Shang , Harsh Patel , Ran Gong , Karl Schmeckpeper

This paper introduces SceneCraft, a Large Language Model (LLM) Agent converting text descriptions into Blender-executable Python scripts which render complex scenes with up to a hundred 3D assets. This process requires complex spatial…

Computer Vision and Pattern Recognition · Computer Science 2024-03-05 Ziniu Hu , Ahmet Iscen , Aashi Jain , Thomas Kipf , Yisong Yue , David A. Ross , Cordelia Schmid , Alireza Fathi

Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus on character consistency, they often overlook the crucial…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Quanjian Song , Donghao Zhou , Jingyu Lin , Fei Shen , Jiaze Wang , Xiaowei Hu , Cunjian Chen , Pheng-Ann Heng

The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial…

Computation and Language · Computer Science 2025-06-27 Xiao Xia , Dan Zhang , Zibo Liao , Zhenyu Hou , Tianrui Sun , Jing Li , Ling Fu , Yuxiao Dong

Existing 4D synthesis methods primarily focus on object-level generation or dynamic scene synthesis with limited novel views, restricting their ability to generate multi-view consistent and immersive dynamic 4D scenes. To address these…

Computer Vision and Pattern Recognition · Computer Science 2025-06-25 Junwei Zhou , Xueting Li , Lu Qi , Ming-Hsuan Yang

Creating scenes for captured motions that achieve realistic human-scene interaction is crucial for 3D animation in movies or video games. As character motion is often captured in a blue-screened studio without real furniture or objects in…

Computer Vision and Pattern Recognition · Computer Science 2024-05-22 Jianan Li , Tao Huang , Qingxu Zhu , Tien-Tsin Wong

Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to…

Computer Vision and Pattern Recognition · Computer Science 2025-11-14 Frédéric Berdoz , Luca A. Lanzendörfer , Nick Tuninga , Roger Wattenhofer

Designing 3D scenes is traditionally a challenging task that demands both artistic expertise and proficiency with complex software. Recent advances in text-to-3D generation have greatly simplified this process by letting users create scenes…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Zeqi Gu , Yin Cui , Zhaoshuo Li , Fangyin Wei , Yunhao Ge , Jinwei Gu , Ming-Yu Liu , Abe Davis , Yifan Ding

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic…

Computer Vision and Pattern Recognition · Computer Science 2026-02-09 Kaiyi Huang , Yukun Huang , Yu Li , Jianhong Bai , Xintao Wang , Zinan Lin , Xuefei Ning , Jiwen Yu , Pengfei Wan , Yu Wang , Xihui Liu

Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of…